SSP is another KGE approach which jointly learns from structured information and entity descriptions. This method adopts the Non-negative Matrix Factorization (NMF) topic model to generate a representation for an entity based on its description, i.e., treating each entity description as a document and taking the topic distribution of the document as the representation of the corresponding entity. However, the approach would not perform well with multilingual entity descriptions since the adopted topic model does not deal with multilinguality.
In LiteralE, entity descriptions are represented using a document embedding technique proposed in [1]}. This document embedding technique works by first mapping the whole document (i.e., entity description) and also every word present in the document into corresponding unique vectors and then taking the average or concatenation of the paragraph vector and word vectors so as to predict the next word in a context.
